Predictive model of postoperative pneumonia after neoadjuvant immunochemotherapy for esophageal cancer

Wang, Wei; Yu, Yonghao; Sun, Haibo; Wang, Zongfei; Zheng, Yan; Liang, Guanghui; Chen, Pei-Nan; Cheng, Jiwei; Xu, Xiaoxia; Yang, Funa; Liu, Qi; Xing, Weiqun

doi:10.21037/jgo-22-149

Cited by 1 publication

(3 citation statements)

References 51 publications

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“… ? Dev: 8715, Ext val: 2147 Dev models Kanda et al, 2019 56 Japan 2005–2017 CTx: 52 MIE/open Dev: 355 NA Wang et al, 2022 32 China 2019–2021 ICTx: 100 MIE/open, TT Dev: 78 NA Reinersman et al, 2016 34 USA 2009–2012 CRTx: 80 Total/hybrid MIE, TH/TT Val: 136 Ferguson Dev development, Val validation, Int internal, Ext external, NA not applicable, CTx chemotherapy, CRTx chemoradiotherapy, ICTx immunochemotherapy, MIE minimal invasive esophagectomy, OE open esophagectomy, TT transthoracal, TH transhiatal, TA thoracoabdominal, RAI risk analysis index, Rev revised, RAI-A administrative risk analysis index, mFI-5 5-factor modified frailty index, STS GTSD Society of Thoracic Surgeons General Thoracic Surgeons Database, aCCI age adjusted Charlson comorbidity index, CCI Charlson Comorbidity Index, ASA American Society of Anesthesiologists, O-POSSUM physiological and operative severity score for the enumeration of mortality and morbidity adjusted for oesophagogastric surgery, ACS NSQIP American College of Surgeons National Surgical Quality Improvement Program, ? unknown …”

Section: Resultsmentioning

confidence: 99%

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Prediction of Morbidity and Mortality After Esophagectomy: A Systematic Review

van Nieuw Amerongen,

de Grooth,

Veerman

et al. 2024

Ann Surg Oncol

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Background Esophagectomy for esophageal cancer has a complication rate of up to 60%. Prediction models could be helpful to preoperatively estimate which patients are at increased risk of morbidity and mortality. The objective of this study was to determine the best prediction models for morbidity and mortality after esophagectomy and to identify commonalities among the models. Patients and Methods A systematic review was performed in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and was prospectively registered in PROSPERO (https://www.crd.york.ac.uk/prospero/, study ID CRD42022350846). Pubmed, Embase, and Clarivate Analytics/Web of Science Core Collection were searched for studies published between 2010 and August 2022. The Prediction model Risk of Bias Assessment Tool was used to assess the risk of bias. Extracted data were tabulated and a narrative synthesis was performed. Results Of the 15,011 articles identified, 22 studies were included using data from tens of thousands of patients. This systematic review included 33 different models, of which 18 models were newly developed. Many studies showed a high risk of bias. The prognostic accuracy of models differed between 0.51 and 0.85. For most models, variables are readily available. Two models for mortality and one model for pulmonary complications have the potential to be developed further. Conclusions The availability of rigorous prediction models is limited. Several models are promising but need to be further developed. Some models provide information about risk factors for the development of complications. Performance status is a potential modifiable risk factor. None are ready for clinical implementation.

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